Three-dimensional feature-preserving noise reduction for real-time electron tomography

Electron tomography (ET) is a method for three-dimensional (3-D) reconstruction of single, transparent objects from a series of images (i.e., a tilt series) recorded with a transmission electron microscope (EM) (Fig. 4.1). The method is related to the procedures used in medical tomography. The 3-D reconstructions are usually computed from the digitized tilt series after a radial weighting scheme has been applied to the Fourier-transformed data. The ET method can be used to reconstruct in 3-D any object that is transparent enough for projection imaging with the transmission EM. This means that specimens of biological origin are usually available for ET 3-D reconstruction, whereas, e.g., colloidal gold particles are not. An ET-calculated 3-D map could be based on projections from objects that have been visualized by EM in several different ways, i.e., from stained or unstained objects, objects visualized at different energy loss levels or objects embedded in different media. The combined analysis of the 3-D structure, imaged in different ways, could thus become very informative. The general applicability also means that it is not restricted to symmetrical or regularly arranged objects, nor to objects with preferred orientations on a support grid. In its present state, the ET method allows reproducible 3-D reconstructions of single molecular objects, with a resolution in the range of 5 nm, of complex cellular specimens. For isolated objects, free of interfering cellular substances, a somewhat higher resolution can be achieved. The ET method covers the intermediate resolution range where there is no other physical technique available to analyze single molecular complexes.

Noise reduction is a basic problem in image processing, binary image contains less information than color images, which means it's more difficult to reduce noise in binary image. The most used noise reduction methods are mathematic morphology and median filter, but they face a common problem that the fine structure information in the image was destroyed in the process of noise reduction. Inspired by the quantum theory, we express an image with quantum system first, after that, a new distance measurement between quantum states was proposed, then we advance a concept, noise feature codebook, to describe the local feature of each pixels in the image. The distribution of the codebook in images with difference noise density was studied, the relationship of the distribution and the noise density was built up by training of masses of images. With the regularity, a noise reduction method was proposed, which choose optimal noise judging criterion according to noise density of the image. Experiment showed that proposed method gain higher SNR and better visual quality than tradition noise reduction methods in most situation.

In electron tomography (ET) based on a transmission electron microscope, the effective thickness of a specimen increases with the tilt angle and, therefore, the projection quality may deteriorate because of electron scattering. The information-missing region, however, can be reduced by broadening the specimen tilt range. To clarify the general influence of these effects on ET, the projection quality varying with the tilt angle was quantitatively evaluated for a 5-microm thick specimen observed with a 3 MV ultrahigh-voltage electron microscope. Simulations of three-dimensional reconstruction were then performed for different specimen thicknesses and tilt ranges. As a result, the ET accuracy was shown to decrease as the specimen thickness increased. However, an optimum specimen-tilt range, at which ET could reach its highest accuracy, was found to exist and become small with the increase of the specimen thickness. The presented results are helpful for determining the specimen thickness limitation on the ET resolution and improving the ET fidelity of thick specimens.

Recently, the double contrast technique in a gastrointestinal examination and the transbronchial lung biopsy in an examination for the respiratory system [1-3] have made a remarkable progress. Especially in the transbronchial lung biopsy, better quality of x-ray fluoroscopic images is requested because this examination is performed under a guidance of x-ray fluoroscopic images. On the other hand, various image processing methods [4] for x-ray fluoroscopic images have been developed as an x-ray system with a flat panel detector [5-7] is widely used. A recursive filtering is an effective method to reduce a random noise in x-ray fluoroscopic images. However it has a limitation for its effectiveness of a noise reduction in case of a moving object exists in x-ray fluoroscopic images because the recursive filtering is a noise reduction method by adding last few images. After recursive filtering a residual signal was produced if a moving object existed in x-ray images, and this residual signal disturbed a smooth procedure of the examinations. To improve this situation, new noise reduction method has been developed. The Adaptive Noise Reduction [ANR] is the brand-new noise reduction technique which can be reduced only a noise regardless of the moving object in x-ray fluoroscopic images. Therefore the ANR is a very suitable noise reduction method for the transbronchial lung biopsy under a guidance of x-ray fluoroscopic images because the residual signal caused of the moving object in x-ray fluoroscopic images is never produced after the ANR. In this paper, we will explain an advantage of the ANR by comparing of a performance between the ANR images and the conventional recursive filtering images.

New Approach To Quantitative Subcellular Imaging Of Phosphorus And Calcium Using Energy-filtered Transmission Electron Microscopy And Tomography

Noise reduction algorithms are widely used to mitigate noise effects on speech to improve the robustness of speech technology applications. However, they inevitably cause speech distortion. The tradeoff between noise reduction and speech distortion is a key concern in designing noise reduction algorithms. This study proposes a novel framework for noise reduction by considering this tradeoff. We regard speech estimation as a function approximation problem in a regularized reproducing kernel Hilbert space (RKHS). In the estimation, the objective function is formulated to find an approximation function by controlling the tradeoff between approximation accuracy and function complexity. For noisy observations, this is equivalent to controlling the tradeoff between noise reduction and speech distortion. Since the target function is approximated in an RKHS, either a linear or nonlinear mapping function can be naturally incorporated in the estimation by a “kernel trick”. Traditional signal subspace and Wiener filtering based noise reduction can be derived as special cases when a linear kernel function is applied in this framework. We first provided a theoretical analysis of the tradeoff property of the framework in noise reduction. Then we applied our proposed noise reduction method in speech enhancement and noisy robust speech recognition experiments. Compared to several classical noise reduction methods, our proposed method showed promising advantages.

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Ultrasound images are contaminated with both additive and multiplicative noise, which is modeled by Gaussian and speckle noise respectively. Distinguishing small features such as fallopian tubes in the female genital tract in the noisy environment is problematic. A new method for noise reduction, Wavelet Median Denoising, is presented. Wavelet Median Denoising consists of performing a standard noise reduction technique, median filtering, in the wavelet domain. The new method is tested on 126 images, comprised of 9 original images each with 14 levels of Gaussian or speckle noise. Results for both separable and non-separable wavelets are evaluated, relative to soft-thresholding in the wavelet domain, using the signal-to-noise ratio and subjective assessment. The performance of Wavelet Median Denoising is comparable to that of soft-thresholding. Both methods are more successful in removing Gaussian noise than speckle noise. Wavelet Median Denoising outperforms soft-thresholding for a larger number of cases of speckle noise reduction than of Gaussian noise reduction. Noise reduction is more successful using non-separable wavelets than separable wavelets. When both methods are applied to ultrasound images obtained from a phantom of the female genital tract a small improvement is seen; however, a substantial improvement is required prior to clinical use.

High-Throughput Immunofluorescence and Electron Tomography to Characterize Centrosomal Aberrations in Plasma Cell Neoplasia

The problem of speech recognition in the presence of interfering nonstationary noise is addressed. A method for noise reduction in the cepstral domain based on a multilayer network is proposed and tested on a large database of isolated words contaminated with nonstationary F-16 jet noise. The speech recognition system consists of an auditory preprocessing module, the cepstral noise reduction multilayer network, and a neural network classifier. The noise reduction network performs a nonlinear autoassociative mapping in the cepstral domain between a set of noisy cepstral coefficients and a set of noise-free cepstral coefficients. The average recognition rate on a test database was improved up to 65% when the noise reduction network was added to the speech recognition system.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Breast cancer is among the most common diseases and one of the most common causes of death in the female population worldwide. Early identification of breast cancer improves survival. Therefore, radiologists will be able to make more accurate diagnoses if a computerized system is developed to detect breast cancer. Computer-aided design techniques have the potential to help medical professionals to determine the specific location of breast tumors and better manage this disease more rapidly and accurately. MIAS datasets were used in this study. The aim of this study is to evaluate a noise reduction for mammographic pictures and to identify salt and pepper, Gaussian, and Poisson so that precise mass detection operations can be estimated. As a result, it provides a method for noise reduction known as quantum wavelet transform (QWT) filtering and an image morphology operator for precise mass segmentation in mammographic images by utilizing an Atrous pyramid convolutional neural network as the deep learning model for classification of mammographic images. The hybrid methodology dubbed QWT-APCNN is compared to earlier methods in terms of peak signal-to-noise ratio (PSNR) and mean square error (MSE) in noise reduction and detection accuracy for mass area recognition. Compared to state-of-the-art approaches, the proposed method performed better at noise reduction and segmentation according to different evaluation criteria such as an accuracy rate of 98.57%, 92% sensitivity, 88% specificity, 90% DSS, and ROC and AUC rate of 88.77.

Time-frequency masking is a method for noise reduction that is based on the time-frequency representation of a speech in noise signal. Depending on the estimated signal-to-noise ratio (SNR), each time-frequency unit is either attenuated or not. A special type of a time-frequency mask is the ideal binary mask (IBM), which has access to the real SNR (ideal). The IBM either retains or removes each time-frequency unit (binary mask). The IBM provides large improvements in speech intelligibility and is a valuable tool for investigating how different factors influence intelligibility. This study extends the standard outcome measure (speech intelligibility) with additional perceptual measures relevant for noise reduction: listening effort, noise annoyance, speech naturalness, and overall preference. Four types of time-frequency masking were evaluated: the original IBM, a tempered version of the IBM (called ITM) which applies limited and non-binary attenuation, and non-ideal masking (also tempered) with two different types of noise-estimation algorithms. The results from ideal masking imply that there is a trade-off between intelligibility and sound quality, which depends on the attenuation strength. Additionally, the results for non-ideal masking suggest that subjective measures can show effects of noise reduction even if noise reduction does not lead to differences in intelligibility.

Both light and electron microscopy have experienced exciting advances in the past few years, driven by the development of new imaging technologies and the ready availability of powerful computers and associated algorithms for visualization, analysis, and image restoration. The concomitant development of molecular genetic methods for engineering fluorescent molecules into proteins and for targeting indicator dyes to subcellular compartments has led to unprecedented opportunities for the study of dynamic processes and for correlating physiology and structure. Although the effective resolution of light microscopy has been increased through the use of optical sectioning and computational deblussing of images, the resolution of the light microscope is still not sufficient to address Inany issues of cell and tissue structure and protein localization. Electron microscopy has also undergone a quiet revolution, in particular high voltage electron microscopy, where the technique of electron tomography allows 3D reconstruction of relatively large structures such as neuronal spiny dendrites without the need for serial sectioning. In combination with light microscopy, high voltage EM and electron tomography provide a relatively straightforward method to bridge the resolution gap between light and electron microscopy. In this review, we discuss correlated 3D light and electron microscopy imaging of cells and tissues, including the adaptation of fluorescent dyes for electron microscopy imaging. (The J Histotechnol 23:261, 2000)

In the current work, irregular morphology of Staphylococcus aureus bacteria has been visualized by phase retrieval employing off-axis electron holography (EH) and 3D reconstruction electron tomography using high-angle annular dark field scanning transmission electron microscopy (HAADF-STEM). Bacteria interacting with gold nanoparticles (AuNP) acquired a shrunken or irregular shape due to air dehydration processing. STEM imaging shows the attachment of AuNP on the surface of cells and suggests an irregular 3D morphology of the specimen. The phase reconstruction demonstrates that off-axis electron holography can reveal with a single hologram the morphology of the specimen and the distribution of the functionalized AuNPs. In addition, EH reduces significantly the acquisition time and the cumulative radiation damage (in three orders of magnitude) over biological samples in comparison with multiple tilted electron expositions intrinsic to electron tomography, as well as the processing time and the reconstruction artifacts that may arise during tomogram reconstruction.

ABSTRACTPrimary Ciliary Dyskinesia (PCD) is a heterogeneous genetic condition characterized by dysfunction of motile cilia. Patients suffer from chronic infection and inflammation of the upper and lower respiratory tract. Diagnosis of PCD is confirmed by identification of a hallmark defect of ciliary ultrastructure or by identification of biallelic pathogenic mutations in a known PCD gene. Since the first description of PCD in 1976, assessment of ciliary ultrastructure by transmission electron microscopy (TEM) has been central to diagnosis and research. Electron tomography is a technique whereby a series of transmission electron micrographs are collected at different angles and reconstructed into a single 3D model of a specimen. Electron tomography provides improved spatial information and resolution compared to a single micrograph. Research by electron tomography has revealed new insight into ciliary ultrastructure and consequently ciliary function at a molecular and cellular level. Gene discovery studies in PCD have utilized electron tomography to define the structural consequences of variants in cilia genes. Modern transmission electron microscopes capable of electron tomography are increasingly being installed in clinical laboratories. This presents the possibility for the use of tomography technique in a diagnostic setting. This review describes the electron tomography technique, the contribution tomography has made to the understanding of basic cilia structure and function and finally the potential of the technique for use in PCD diagnosis.